Download AOSC 200 Weather and Climate

Forecasting
AOSC 200
Tim Canty
Class Web Site: http://www.atmos.umd.edu/~tcanty/aosc200
Topics for today:
Forecasting
Lecture 20
Nov 8, 2016
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1
Regions of Cyclogenesis
East Coast Lows
Gulf Low:
Form along southern coast. Cold land and warm water
means good probability of precipitation
Hatteras Low (Nor’easter):
Form along eastern coast. Warm gulf stream water provides
warm air and moisture that interacts with cold air on land.
If the pressure of a Hatteras Low drops by 24mb in 24 hrs, it’s
considered a “bomb”
Can produce hurricane strength winds, flooding, heavy snow.
Can last for days!!!
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2
Nor’easters
Fig 8.U1:
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Essentials of Meteorology
3
Cyclogenesis in 3-D
Surface high pressure directed to southeast by jet stream
Surface low pressure directed northeast by jet stream
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4
Cyclogenesis in 3-D
Fig 8.32:
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Essentials of Meteorology
5
Cyclogenesis in 3-D
Fig 8.32:
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Essentials of Meteorology
6
Cyclogenesis in 3-D
Fig 8.32:
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Essentials of Meteorology
7
Cyclogenesis in 3-D
Fig 8.33:
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Essentials of Meteorology
8
Cyclogenesis in 3-D
Fig 8.33:
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Essentials of Meteorology
9
Rossby Waves
Zonal Flow: Jet stream moves from west to east
without meandering around much
Fig 7-15 Meteorology: Understanding the Atmosphere
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10
Rossby Waves
Meridional Flow: Jet stream wanders around a lot
as the wind moves from west to east
Sometimes happens when a large pacific storm
hits the jet stream
Fig 7-15 Meteorology: Understanding the Atmosphere
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11
Rossby Waves
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12
Weather Forecasting
People have been trying to predict the weather for
thousands of years.
In the past, forecasts were based on experiences
and passed down from one generation to the next
“Red sky at night, sailor’s delight
Red sky in the morning, sailors take warning”
This is an example of a folklore forecast but what
does it mean?
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13
Folklore forecast
“The higher the clouds, the finer the weather”
“Ring around the moon? Rain or snow soon”
–
These forecasts don’t have to rhyme
“Cows lay down ahead of storms”
“Wooly caterpillars predict severe winters”
“The fat squirrel survives the winter”
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14
Folklore
F
olklore forecast
fore
ecast
“The
“
The h
higher
igher tthe
he c
clouds,
louds, tthe
he ffiner
iner tthe
he w
weather”
eather”
““Ring
Ring a
round tthe
he m
oon? R
ain o
now s
oon”
around
moon?
Rain
orr s
snow
soon”
–
T
hese fforecast
orecast d
on’t have
have tto
o rhyme
rhyme
These
don’t
C
ows llay
ay d
own a
head o
torms
Cows
down
ahead
off s
storms
W
ooly c
aterpillars predict
predict severe
severe winters
winters
Wooly
caterpillars
T
he ffat
at squirrel
squirrel survives
survives the
the winter
winter
The
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15
Cold Fronts
Represented as blue triangles pointing toward warm air
Travels ~25 knots
Warm air forced up, cools, releases latent heat, leads to storms
Fig 8.17:
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Essentials of Meteorology
16
Warm Fronts
Represented as red semi-circles pointing toward cold air
Travels ~10 knots
Warm air slides up cold air and slowly cools.
Clouds form starting with cirrus, then various layers of stratus
Fig 8.20:
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Essentials of Meteorology
17
Persistence forecast
“The weather tomorrow will be like the weather today”
http://forecast.weather.gov/MapClick.php?lat=38.98881034861978&lon=-76.94056627621188#.VkH-6dKrTVQ
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18
Probability forecast
“The probability of weather on a specific day will be
like the long–term average of weather for that day”
Probability of snow on
December 25th
Assumes short term
variations are
“averaged out” and
there is little long-term
variation in climate.
Fig 9.8:
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Essentials of Meteorology
19
Climatology forecast
“The weather this season will be like the long–term
average of weather for this season”
http://www.erh.noaa.gov/lwx/Historic_Events/DC-Winters.htm
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20
Types of Forecasts
Folklore: based on traditional proverbs, sometimes
accurate. Uses behavior of animals and other
creatures as predictors of future weather.
Persistence: assumes that the weather will not
exhibit large day to day fluctuations. The weather
tomorrow will be like the weather today
Probability/Climatology: assumes the weather for a
day or a season will be close to the average weather
for that day or season.
ĸWide brown stripe = mild winter
Cows lie down before weather to
save warm, dry spot
ĺ
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21
Problems with these forecasts
Persistence: we know that, at some point, this will be
wrong because eventually the weather WILL change
Probability/Climatology: we know that, at some point,
this will be wrong because eventually the weather
WILL deviate from the average
We know that weather changes at a particular spot
because weather features move… but do the weather
features themselves change?
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22
Trend Forecast
Recognizes that weather causing patterns move but
assumes the following remain unchanged:
• speed
• intensity
• size
• direction
http://ww2010.atmos.uiuc.edu/(Gl)/guides/mtr/fcst/mth/trnd.rxml
23
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Trend Forecast
Recognizes that weather causing patterns move but
assumes the following remain unchanged:
• speed
• intensity
• size
• direction
Forecast fails if any of our assumptions are no longer
valid
For example, the system dies out, slows down, etc.
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24
Analog Forecast
Recognizes that weather causing patterns change
but assumes:
• weather will always behave the same way under
a specific set of conditions
In other words, weather repeats itself
If you find the last time current conditions existed,
you can use the historical data to determine how
conditions will change
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25
Analog Forecast
The weather today is “analogous” to weather at a time in the past
Fig 13-4 Meteorology: Understanding the Atmosphere
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26
Statistical Forecast
Uses Model Output Statistics (MOS)
Statistically weighted analog forecast
• looks at model output that best forecasted past
events to make future predictions
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27
Numerical Weather Prediction
Astronomers could predict eclipses 100’s of years
before meteorologist started to predict weather
Why?
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28
Numerical Weather Prediction: Observations
Step 1: Collect observations
• ASOS
• sondes
• ships
• buoys
• aircraft
• satellites
• wind profilers
• etc.
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29
Numerical Weather Prediction: Assimilation
Step 2: Data Assimilation
• Data does not cover the entire globe at all times
• Data is smoothed and interpolated to model grid
Fig 13-13 Meteorology: Understanding the Atmosphere
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30
Numerical Weather Prediction: Assimilation
Step 2: Data Assimilation
• Data does not cover the entire globe at all times
• Data is smoothed and interpolated to model grid
= Observations
Fig 13-13 Meteorology: Understanding the Atmosphere
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31
Numerical Weather Prediction: Integration
Step 3: Model Integration
• Assimilated data is used to solve equations that
describe the atmosphere
• Determines state of atmosphere at next time step
= Observations
Fig 13-13 Meteorology: Understanding the Atmosphere
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32
Numerical Weather Prediction: Integration
Step 3: Model Integration
• Assimilated data is used to solve equations that
describe the atmosphere
• Determines state of atmosphere at next time step
More grid points
(higher resolution)
yields more accurate
forecasts.
= Observations
Takes longer to run,
though.
Fig 13-13 Meteorology: Understanding the Atmosphere
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33
Numerical Weather Prediction: Tweaking
Step 4: Tweaking and Broadcasting
• Analyze model output accounting for known
biases in models
• Combine model output with knowledge of local
weather (small scale winds that models can’t
predict) to create forecasts
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34
Forecasting (Surface Map)
Fig 9.14:
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Essentials of Meteorology
35
Forecasting (500 mb Map)
Fig 9.15:
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Essentials of Meteorology
36
Forecasting (Future Surface)
Fig 9.16:
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Essentials of Meteorology
37
Actual weather (+1 day)
Fig 9.17:
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Essentials of Meteorology
38
Forecast Range
Nowcasting
A description of current weather parameters
and 0-2 hours description of forecasted
weather parameters
Very short-range weather
forecasting
Up to 12
parameters
Short-range weather forecasting
Beyond 12 hours and up to
description of weather parameters
hours
description
of
72
weather
hours
Beyond 72 hours and up to 240 hours
Medium-range weather forecasting description of weather parameters
Extended-range weather
forecasting
Beyond 10 days and up to 30 days description
of weather parameters, usually averaged and
expressed as a departure from climate values
for that period.
Long-range forecasting
From 30 days up to two years
http://www.wmo.int/pages/prog/www/DPS/GDPS-Supplement5-AppI-4.html
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39
Ensemble Forecasts
Ensemble forecasts:
Run model numerous times for slightly different
initial conditions
Perform statistical analysis of all the model runs
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40
Ensemble Forecasts
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41
Forecast Errors
Fig 13-25 Meteorology: Understanding the Atmosphere
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42
Lecture Recap
• Weather Forecasting
• Folklore
• Persistence
• Probability/Climatology
• Trend
• Analogue
• Statistical
• Numerical
• Numerical Weather Prediction
• Observations
• Data assimilation
• Model integration
• Tweaking and Broadcasting
• Ensemble Forecasts
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43